Method to evaluate trajectory candidates for autonomous driving vehicles (ADVs)

ABSTRACT

In one embodiment, a system generates a plurality of trajectory candidates for an autonomous driving vehicle (ADV) from a starting point to an end point of a particular driving scenario. The system generates a reference trajectory corresponding to the driving scenario based on a current state of the ADV associated with the starting point and an end state of the ADV associated with the end point, where the reference trajectory is associated with an objective. For each of the trajectory candidates, the system compares the trajectory candidate with the reference trajectory to generate an objective cost representing a similarity between the trajectory candidate and the reference trajectory. The system selects one of the trajectory candidates as a target trajectory for driving the ADV based on objective costs of the trajectory candidates.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to methods to evaluate trajectory candidates for autonomousdriving vehicles (ADVs).

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

An ADV can self-navigate using a driving trajectory. A drivingtrajectory can be divided into a longitudinal component and a lateralcomponent. The longitudinal component or longitudinal trajectory refersto vehicle motions running lengthwise along a predetermined path (e.g.,a station path). A longitudinal trajectory can determine a place, aspeed, and acceleration for the ADV at a given point in time. Thus,longitudinal trajectory generation is a critical component for a semi orfully-autonomous driving vehicle.

There are several factors that need to be considered for a longitudinaltrajectory generation process. Some factors can be: safety and comfortfor onboard passengers of the ADV and/or nearby pedestrians, and trafficrule following factors. To achieve a safe operation of the ADV, atrajectory generation process needs to account for obstacles in thesurrounding environment. For a comfortable operation, the trajectoriesneed to be to be a smooth and efficient trajectory, i.e., trajectorieswith graceful accelerations which can maneuver an ADV from a currentlocation to a destination within a reasonable time. Lastly, thetrajectories need to follow local traffic rules, i.e., stop at redsignal lights and stop signs, etc. There has been a lack of efficientways to generate a trajectory considering all of these factorsappropriately.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 is a block diagram illustrating an example of a trajectorygeneration module according to one embodiment.

FIG. 5 is a block diagram illustrating an example scenario for an ADVaccording to one embodiment.

FIG. 6A is an example of a velocity-time graph illustrating a number oftrajectories according to one embodiment.

FIG. 6B is an example of a station-time graph illustrating a number oftrajectories according to one embodiment.

FIG. 7 is a block diagram illustrating an example of a trajectoryevaluation module according to one embodiment.

FIG. 8 is an example of a station-time graph illustrating a trajectorycandidate according to one embodiment.

FIGS. 9A-9C are examples of reference velocity curves according to someembodiments.

FIG. 10 is an example of a velocity-time graph illustrating an objectiveevaluation according to one embodiment.

FIG. 11 is a flow diagram illustrating a method performed by an ADVaccording to one embodiment.

FIG. 12 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to one aspect, a system generates a plurality of trajectorycandidates for an autonomous driving vehicle (ADV) from a starting pointto an end point of a particular driving scenario. The system generates areference trajectory corresponding to the driving scenario based on acurrent state of the ADV associated with the starting point and an endstate of the ADV associated with the end point, where the referencetrajectory is associated with an objective. For each of the trajectorycandidates, the system compares the trajectory candidate with thereference trajectory to generate an objective cost representing asimilarity between the trajectory candidate and the referencetrajectory. The system selects one of the trajectory candidates as atarget trajectory for driving the ADV based on objective costs of thetrajectory candidates.

In comparing each trajectory candidate with the reference trajectory,according to one embodiment, the trajectory candidate into a number ofcandidate segments. The reference trajectory is segmented into a numberof reference segments. For each trajectory segment, a velocitydifference between the velocity of the candidate segment and thecorresponding reference segment is determined. That is, velocities of acandidate segment and a reference segment corresponding to the samepoint in time are compared. The objective cost is calculated based onthe velocity differences between the candidate segments and theassociated reference segments, for example, using a predeterminedalgorithm. In a particular embodiment, the objective cost is calculatedby summing squared velocity differences between the trajectory segmentsand the reference segments.

According to another aspect, for each of the trajectory candidates, anobstacle closest to the trajectory candidate is identified. A distancebetween the trajectory candidate and the obstacle is measured. A safetycost associated with the trajectory candidate is calculated based on thedistance between the trajectory candidate and the obstacle, for example,using a safety cost algorithm or function. A total trajectory cost forthe trajectory candidate is then calculated based on the objective costand the safety cost. The target trajectory is selected from thetrajectory candidates based on the total trajectory cost, e.g., thelowest total trajectory cost.

According to a further aspect, for each of the trajectory candidates, achanging rate of the acceleration along the trajectory candidate isdetermined. The acceleration changing rate can be determined based on aderivative of the speed along the trajectory candidate. A comfort costfor the trajectory candidate is then calculated based on theacceleration changing rate, for example, using a comfort cost algorithmor function. The total trajectory cost is then calculated based on theobjective cost and the comfort cost and the target trajectory can beselected based on the total trajectory cost.

In calculating a comfort cost of a trajectory candidate, according toone embodiment, the trajectory candidate is segmented into a number ofcandidate segments. For each candidate segment, a segment comfort costfor the candidate segment is calculated based on a changing rate of theacceleration associated with the candidate segment. The comfort cost isthen calculated based on the segment comfort costs of all the candidatesegments using a predetermined algorithm. In a particular embodiment,the comfort cost is calculated based on a sum (e.g., a squared sum) ofthe segment comfort costs of the candidate segments.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) severs, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn control the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyword, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,weather conditions, and road conditions, such as slow traffic onfreeway, stopped traffic, car accident, road construction, temporarydetour, unknown obstacles, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or models 124 for a variety ofpurposes, including trained models to determine if an obstacle detectedby sensors of the ADV is a stop sign or a traffic light.Rules/algorithms 124 may further include traffic rules for the ADV tofollow and algorithms to calculate a quartic and/or a quintic polynomialfor a trajectory. Algorithms 124 may further include algorithms togenerate trajectory candidates as a final trajectory. Algorithms 124 mayfurther include algorithms to calculate trajectory costs for selecting atarget trajectory from the trajectory candidates, including algorithmsto calculate objective costs, safety costs, and comfort costs.Algorithms 124 can then be uploaded onto ADVs for real-time trajectorygeneration for autonomous driving.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing module 307, trajectory generation module 308, andtrajectory evaluation module 309.

Some or all of modules 301-309 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-309may be integrated together as an integrated module. For example,trajectory generation module 308 and/or trajectory evaluation module 309may be implemented as a part of decision module 304 and/or planningmodule 305. Decision module 304 and planning module 305 may be anintegrated module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration (e.g., straight or curvelanes), traffic light signals, a relative position of another vehicle, apedestrian, a building, crosswalk, or other traffic related signs (e.g.,stop signs, yield signs), etc., for example, in a form of an object.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to entering theintersection. If the perception data indicates that the vehicle iscurrently at a left-turn only lane or a right-turn only lane, predictionmodule 303 may predict that the vehicle will more likely make a leftturn or right turn respectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal route in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 as a basis. That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, and turning commands) atdifferent points in time along the path or route.

The planning phase may be performed in a number of planning cycles, alsoreferred to as command cycles, such as, for example, in every timeinterval of 100 milliseconds (ms). For each of the planning cycles orcommand cycles, one or more control commands will be issued based on theplanning and control data. That is, for every 100 ms, planning module305 plans a next route segment or path segment, for example, including atarget position and the time required for the ADV to reach the targetposition. Alternatively, planning module 305 may further specify thespecific speed, direction, and/or steering angle, etc. For example,planning module 305 may plan a route segment or path segment for thenext predetermined period of time such as 5 seconds. For each planningcycle, planning module 305 plans a target position for the current cycle(e.g., next 5 seconds) based on a target position planned in a previouscycle. Control module 306 then generates one or more control commands(e.g., throttle, brake, steering control commands) based on the planningand control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to effect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

Decision module 304/planning module 305 may further include a collisionavoidance system or functionalities of a collision avoidance system toidentify, evaluate, and avoid or otherwise negotiate potential obstaclesin the environment of the autonomous vehicle. For example, the collisionavoidance system may effect changes in the navigation of the autonomousvehicle by operating one or more subsystems in control system 111 toundertake swerving maneuvers, turning maneuvers, braking maneuvers, etc.The collision avoidance system may automatically determine feasibleobstacle avoidance maneuvers on the basis of surrounding trafficpatterns, road conditions, etc. The collision avoidance system may beconfigured such that a swerving maneuver is not undertaken when othersensor systems detect vehicles, construction barriers, etc. in theregion adjacent the autonomous vehicle that would be swerved into. Thecollision avoidance system may automatically select the maneuver that isboth available and maximizes safety of occupants of the autonomousvehicle. The collision avoidance system may select an avoidance maneuverpredicted to cause the least amount of acceleration in a passenger cabinof the autonomous vehicle.

According to one embodiment, trajectory generation module 308 cangenerate longitudinal trajectory candidates for ADV 101. The trajectorycandidates may be generated in view of factors such as safety, comfort,and traffic rules. Trajectory evaluation module 309, also referred to astrajectory selection module, can then select one of the generatedtrajectory candidates as the trajectory to control the ADV based onevaluation criteria 315 and using cost functions 316. Trajectoryselection processes will be described in details further below.Trajectory generation module 308 may be implemented as part of decisionmodule 304 and/or planning module 305. In one embodiment, trajectorygeneration module 308 can, in response to perceiving an environment,generate in real-time a station-time graph. Trajectory generation module308 can then projects any perceived obstacles in that instance onto thestation-time graph. Based on a boundary of the projected obstacles, endconditions can be enumerated. Trajectory candidates can then begenerated based on these end conditions. A trajectory candidate can beselected as a final trajectory to control the ADV.

FIG. 4 is a block diagram illustrating an example of a trajectorygeneration module according to one embodiment. Referring to FIG. 4,trajectory generation module 308 can include initial conditiondeterminer 401, scenario determiner 403, graph generator 405, endconditions determiner 407, trajectory candidate generator 409, andtrajectory candidate selector 411. Initial condition determiner 401 candetermine an initial condition for the ADV. Scenario determiner 403 candetermine a driving scenario for the ADV in view of obstacles perceivedby the ADV. Graph generator 405 can generate a station-time graph and/ora velocity-time graph for the ADV. End conditions determiner 407 candetermine all possible end conditions for the ADV based on a drivingscenario and the initial condition of the ADV and other factors such assafety, comfort, and traffic rules. Trajectory candidate generator 409can generate trajectory candidates from the possible ends conditions.Trajectory candidate selector 411 can select a trajectory candidate froma pool of trajectory candidates.

FIG. 5 is a block diagram illustrating an example of a scenario for anADV according to one embodiment. Referring to FIG. 5, scenario 500includes ADV 101 along path 505. Path 505 may be a path (or a segment ofa path) predetermined by decision 304 and/or planning module 305 whichcan maneuver ADV 101 from an initial point to a final destination. ADV101 can perceive a surrounding environment including vehicle 504 ahead,a red traffic light 506 ahead, and vehicle 502 (which may be predicted,by prediction module 303 of ADV 101, to have some chance to swerve infront of ADV 101).

Referring to FIGS. 4-5, in one embodiment, an initial conditiondeterminer, such as initial condition determiner 401 of ADV 101, candetermine an initial condition of ADV 101 based on a current vehiclestate. A vehicle state can include a position, a velocity, acceleration,a curvature (e.g., heading direction), and a curvature change rate ofthe ADV. Here, initial condition determiner 401 can determine an initial(e.g., current) condition for a longitudinal position (s_init), alongitudinal velocity (s_dot_init), and a longitudinal acceleration(s_ddot_init) of ADV 101.

Based on the initial condition, in one embodiment, graph generator 405generates a longitudinal velocity-time (or velocity-time or VT) graphhaving a predetermined time period, i.e., a maximum planning timeinterval for the current planning cycle (e.g., 100-200 ms). FIG. 6A isan example of a velocity-time graph according to an embodiment. FIG. 6Acan be a velocity-time graph generated by graph generator 405 tocorrespond to scenario 500 of FIG. 5. Referring to FIG. 6A, VT graph 600includes initial point 601 and a number of end points (such as points603-605) correspond to a number of possible end conditions. In oneembodiment, the number of end conditions can be enumerated based onfixed time intervals and/or speed increments. In another embodiment, thenumber of end conditions can be enumerated based on a time intervalpredefined by a user. In another embodiment, the number of endconditions can be capped by map information and/or traffic rules, suchas a maximum and/or a minimum speed limit for a stretch of road.

Referring to FIG. 6A, point 601 can correspond to an initiallongitudinal velocity (e.g., s_dot_init) for ADV 101, at time t=0.Points 603 and 605 can correspond to two enumerated end conditions,which can represent end points of two possible trajectory candidates.Point 603 can correspond to an end condition having a velocity of zeroafter some time and point 605 can correspond to an end condition havinga higher velocity than the initial longitudinal velocity after sometime. The end conditions generated using the velocity-time graph canrepresent a first group (or a second set) of end conditions. Each of theend conditions in the first group (or the second set) can correspond toan end longitudinal velocity (s_dot_end) and longitudinal acceleration(s_ddot_end). Note, in some embodiments, end longitudinal accelerationis always equal to zero.

Next, scenario determiner 403 determines a current driving scenario forADV 101. In one embodiment, the possible driving scenarios include, butare not limited to: cruise, follow a leading vehicle, and stopscenarios. A current driving scenario can be determined based on acurrent traffic condition along path 505, for example, by perceptionmodule 302 and/or prediction module 303, which can be determined basedon the identified obstacles along path 505. For example, if a plannedpath of ADV 101 is without any obstacles, then the driving scenario canbe a cruise scenario. If a planned path of ADV 101 includes both static(e.g., non-moving) and dynamic (e.g., moving) obstacles, such as a stopsign and a moving vehicle, then the driving scenario can be a follow aleader vehicle scenario. If a planned path includes only staticobstacles, e.g., stop signs and/or traffic lights, then the drivingscenario can be a stop scenario. Here, scenario 500 includes both staticand dynamic obstacles and a current driving scenario can be determinedto be a follow scenario.

In one embodiment, graph generator 405 generates a station-time graphfor a follow or a stop scenario but not for a cruise scenario. A cruisescenario may not be associated with a station-time graph (also referredto as a path-time graph or a path-time space) because there are noperceived obstacles in a cruise scenario. In one embodiment, for afollow or a stop scenario, graph generator 405 generates a path-time (orstation-time) graph based on the initial condition. The station-timegraph is generated for a predetermined time period, such that there isenough time to take into account the possible longitudinal trajectoriesof the ADV until a next planning cycle. In one embodiment, graphgenerator 405 generates geometric regions for obstacles perceived by ADV101 by projecting these obstacles onto the generated station-time graph.These regions in the path-time space may include projections for staticand dynamic obstacles such as predicted trajectories of another vehicle(e.g., vehicle 502) swerving into path 505.

FIG. 6B shows an example of a station-time graph according to anembodiment. FIG. 6B can be a station-time graph generated by graphgenerator 405 to correspond to scenario 500 of FIG. 5. Referring to FIG.6B, for example, point 611 can correspond to s_init of ADV 101. Graphgeneration 405 can generate station-time obstacles (e.g., regions) 612,614, and 616. Region 612 can correspond to obstacle (or vehicle) 502which may be predicted to swerve in front of ADV 101. Region 614 cancorrespond to vehicle 504 cruising ahead of ADV 101 and region 616 cancorrespond to red traffic light 506.

Next, end conditions determiner 407 can enumerated end conditions basedon the generated station-time graph. In one embodiment, end conditionscan be enumerated based on a boundary of each of the regions in thestation-time graph. In another embodiment, end conditions can beenumerated based on only an upper and/or a lower boundary of each of theregions in the station-time graph. For example, ST points 621-622 alongthe upper and/or the lower boundary of region 612 can be enumerated asend conditions for region 612. ST point 624 along a lower boundary ofregion 614 can be enumerated as an end condition for region 614. STpoints 626-627 along a lower boundary of region 616 can be enumerated asend conditions for region 616. Here, because ADV 101 would violate atraffic rule and/or hit an obstacle if ADV 101 touches or intersectsregions 612, 614, 616, end conditions lying on regions 612, 614, 616need not be enumerated. In one embodiment, the number of end conditionscan be enumerated based on fixed time intervals. In another embodiment,the number of end conditions can be enumerated based on a time intervalpredefined by a user. The generated end conditions using thestation-time graph can represent a second group (or a first set) of endconditions. Each of the end conditions in the second group (or the firstset) can correspond to an end station position (s_end), longitudinalvelocity (s_dot_end), and longitudinal acceleration (s_ddot_end). Note,in some embodiments, end longitudinal acceleration is always equal tozero.

Once the second group (or the first set) of end conditions isdetermined, in one embodiment, trajectory candidate generator 409generates a number of trajectory candidates based on the end conditionsfrom the first group (or the second set) and the second group (or thefirst set) by curve fitting these end conditions with theircorresponding initial conditions to a quartic (4^(th) order) and/or aquintic (5^(th) order) polynomial using a curve fitting algorithm forsmoothness. Referring to FIG. 6A (e.g., the VT graph), for example,trajectory candidate generator 409 can generate trajectory candidates609 by applying a curve fitting algorithm to fit a quartic polynomial toinitial point 601 and end point 605. In another example, referring toFIG. 6B (e.g., the ST graph), trajectory candidate generator 409 cangenerate trajectory candidate 615 by applying a curve fitting algorithmto fit a quintic polynomial to initial point 611 and end point 621. Forthis example, trajectory candidate 615 corresponds to ADV 101 overtakinga predicted trajectory of vehicle 502.

Once trajectory candidates are generated from all possible endconditions, in one embodiment, an evaluation module, such as evaluationmodule 309 of FIG. 3A, evaluates each of the trajectory candidates for abest trajectory candidate. Trajectory candidate selector 411 can thenselect this best trajectory candidate to control the ADV.

Referring back to FIG. 3A, trajectory evaluation module 309 may beimplemented in software, hardware, or a combination thereof. Forexample, trajectory evaluation module 309 may be installed in persistentstorage device 352, loaded into memory 351, and executed by one or moreprocessors (not shown). The trajectory evaluation module 309 may becommunicatively coupled to or integrated with some or all modules ofvehicle control system 111 of FIG. 2. In one embodiment, trajectoryevaluation module 309 and trajectory generation module 308 may be anintegrated module.

FIG. 7 is a block diagram illustrating an example of a trajectoryevaluation module according to one embodiment. Referring to FIG. 7,trajectory evaluation module 309 can evaluate trajectory candidatesbased on cost functions to evaluate trajectory candidates for selectionof a best trajectory to control an ADV. Trajectory evaluation module 309can include trajectory segmenter 701, comfort cost determiner 703,safety cost determiner 705, reference velocity curves generator 707, andobjective/rule following cost determiner 709. Trajectory segmenter 701can segment a candidate trajectory into a number of segments. Comfortcost determiner 705 can determine a comfort cost for a candidatetrajectory. Safety cost determiner 703 can determine a safety cost for acandidate trajectory. Reference velocity curves generator 707 cangenerate reference velocity-time curves (which follows traffic rules) toevaluate an objective following cost for a candidate trajectory.Objective/rule following cost determiner 709 can determine an objectivecost for a candidate trajectory.

A number of criteria (or costs) can be considered for evaluation oftrajectory candidates, such as, for example, safety, comfort, rulefollowing, and/or objective following criteria, which may be defined asa part of evaluation criteria 315. A safety cost can be calculated byconsidering whether the trajectory candidate will lead the autonomousvehicle to collide with other vehicles/obstacles in the road. A comfortcost can be calculated by considering whether passengers the ADV willhave a comfortable riding experience when the ADV uses the trajectorycandidate. A rule following cost can be calculated by consideringwhether the autonomous vehicle obeys traffic rules. For example, itrequires the vehicle to stop at certain places, e.g., before stop signsand/or red traffic lights, etc. An objective following cost cancalculated by considering whether the ADV will finish the transportationtask in a timely manner, i.e., whether the trajectory candidate cantransport passengers to a destination without spending an excessivetime. In one embodiment, the different evaluation criteria can bemodeled as cost functions 316. An example cost function (or overall costfunction) can be cost_overall=sum (costs*weight), where costs can be acost for comfort, safety, rule following, and/or objective following,and weight is a weighting factor for each of the corresponding costs.

Referring to FIGS. 7 and 8, in one embodiment, a safety cost can beevaluated (via safety cost determiner 703) using a station-time graph,such as station-time graph 800 of FIG. 8. Station-time graph 800 can bestation-time graph 610 of FIG. 6B having obstacles 612, 614, 616, andtrajectory candidate 615 to be evaluated for safety. In one embodiment,trajectory candidate 615 is segmented, by trajectory segmenter 701, intoa number of segments 801 according to a predetermined time resolution.Each of the segments 801 can have an associated end point 803 (e.g., atimestamp). Trajectory segmenter 701 then bounds end points 803 (ortimestamps) with boxes 805. Boxes 805 can have a predetermined widththat can represent a width of the ADV. In one embodiment, safety costdeterminer 703 traverses each of the boxes 805 to determine if any edgesof boxes 805 would intersect a boundary of any static/dynamic obstaclesin station-time graph 800, e.g., obstacles 612, 614, 616. If there is anintersection (e.g., a possible collision) then the trajectory candidatemay be removed from selection. In another embodiment, a closest distancefrom trajectory candidate 615 to any boundaries of obstacles 612, 614,615 can be calculated along all timestamps of trajectory candidate 615.The safety cost can be an inverse of the calculated distance, e.g., acloser distance has a higher cost. In this case, a trajectory candidatewhich is far away from surrounding obstacles, e.g., a lower cost, may bedeemed safer than a trajectory candidate which may be closer to thesurrounding obstacles.

A comfort cost can be determined (via comfort cost determiner 705) bycalculating a sum value for jerk for each of the time periods, such as asquared sum. Jerk is defined as a change in acceleration of the ADV.Here, a jerk value can be computed by calculating a change ofacceleration over the time period for neighboring timestamps, e.g., endpoints 803. The jerk values for each time period are then squared andsummed together for a comfort cost.

To determine the objective/rule following cost (also simply referred toas an objective cost), in one embodiment, a reference velocity curvemust first be generated. A reference velocity is an ideal speed curvefor an ADV absent any obstacles. In other words, a reference velocitycurve is a velocity curve in which the ADV should strive to achieve.Without a reference velocity curve, ADV 101 would have no referencespeeds to compare with and ADV 101 can be in standstill and would stillsatisfy the safety and comfort evaluation criteria, e.g., ADV 101 not inmotion will be safe and comfortable. However with a reference velocitycurve to follow, ADV 101 can strive to arrive at a predefineddestination in the least amount of time while ensuring a comfortabledrive.

To generate a reference velocity curve, in one embodiment, there are atleast two scenarios to be considered. A first scenario is where there isno obstacle and the objective of the ADV is to cruise at a steady speed,e.g., v_cruise. In this case, reference curve generator 707 can generatea reference velocity curve to be a constant speed curve such as curve900 of FIG. 9A. In one embodiment, the constant speed or the cruisespeed is determined based on a road speed limit. In another embodiment,the constant speed or the cruise speed is determined based on a speedpredefined by a user of the ADV. For a second scenario where there is anobstacle corresponding to a stopping point, i.e., the ADV needs to stopat a point with a known position, reference curve generator 707 candetermine a comfortable deceleration d_comf, which can be a maximaldeceleration d_max of the vehicle times a predetermined comfort factorf. Based on d_comf, reference curve generator 707 can compute adeceleration trajectory (e.g., 912 of FIG. 9B) that can slow down theADV with the comfortable decleration, e.g., d_comf, and stop the ADV atthe known stopping point.

In one embodiment, based on the calculated deceleration trajectory(e.g., 912), if the ADV cannot slowdown from a cruise speed v_cruiseusing d_comf then reference curve generator 707 generates a constantdeceleration trajectory from the current position of the ADV to the stoppoint. Reference velocity curve 921 of FIG. 9C is an example of such areference velocity curve.

In another embodiment, based on the calculated deceleration trajectory(e.g., 912), if ADV can slowdown from a cruise speed v_cruise usingd_comf then reference curve generator 707 generates a curve with aconstant speed trajectory (e.g., v_cruise) in a first portion, followedby a constant decelerating trajectory (e.g., 912) in a second portion.Reference velocity curve 911 of FIG. 9B is an example of such areference velocity curve. Once the reference speed curve is generatedfor a trajectory candidate, the objective/rule following cost can thenbe determined.

FIG. 10 is an example of a velocity-time graph illustrating an objectiveevaluation according to one embodiment. Referring to FIG. 10,velocity-time graph 1000 includes velocity-time trajectory 1015 andreference velocity curve 911. Trajectory 1015 may represent trajectorycandidate 615 of FIG. 8 in a velocity-time domain. Reference velocitycurve 911 may be a reference velocity curve generated for trajectorycandidate 615 in view of a stopping point (e.g., red traffic light 506of FIG. 5).

In one embodiment, objective/rule following cost determiner 709 firstcomputes a difference curve (e.g., curve 1001) based on differences(e.g., 1005) for trajectory 1015 and reference velocity curve 911 ateach points (or timestamps) (1003). Velocity values of difference curve1005 at each of the timestamps are then summed (e.g., square-summed) tobe the objective/rule following cost. Here, the objective/rule followingcost represents a “closeness” of a trajectory candidate to a referencetrajectory.

In one embodiment, considering safety, comfort, objective following, andrule following costs, an overall or total cost function can be:cost_overall=cost_safety*weight_safety+cost_comfort*weight_comfort+cost_obj*weight_obj.The cost_safety is a safety cost and weight_safety is a weight factorfor the safety cost. The cost_comfort is a comfort cost andweight_comfort is a weight factor for the comfort cost. The cost_obj isan objective cost and weight_obj is a weight factor for the objectivecost. Note the different weight factors can be numerical values toadjust for an importance of each of the costs relative to the rest ofthe costs. Once overall costs are computed for each of the trajectorycandidates, a best trajectory candidate can be selected based on alowest overall cost among the trajectory candidates.

FIG. 11 is a flow diagram illustrating a method performed by an ADVaccording to one embodiment. Processing 1100 may be performed byprocessing logic which may include software, hardware, or a combinationthereof. For example, process 1100 may be performed by trajectorygeneration module 308 and/or trajectory evaluation module 309 of FIG.3A. Referring to FIG. 11, at block 1101, generates a number oftrajectory candidates for an autonomous driving vehicle (ADV) from astarting point to an end point of a particular driving scenario. Atblock 1102, processing logic generates a reference trajectorycorresponding to the driving scenario based on a current state of theADV associated with the starting point and an end state of the ADVassociated with the end point, where the reference trajectory isassociated with an objective. At block 1103, for each of the trajectorycandidates, processing logic compares the trajectory candidate with thereference trajectory to generate an objective cost representing asimilarity between the trajectory candidate and the referencetrajectory. At block 1104, processing logic selects one of thetrajectory candidates as a target trajectory for driving the ADV basedon objective costs of the trajectory candidates.

In one embodiment, comparing the trajectory candidate with the referencetrajectory includes segmenting the trajectory candidate into a pluralityof candidate segments, segmenting the reference trajectory into aplurality of reference segments, for each of the candidate segments,calculating a velocity difference between a velocity of the candidatesegment and a velocity of a corresponding reference segment, andcalculating the objective cost based on the velocity differences betweenthe candidate segments and reference segments. In another embodiment,the objective cost is calculated by summing squared velocity differencesbetween the trajectory segments and the reference segments.

In one embodiment, for each of the trajectory candidates, processinglogic further identifies an obstacle that is closest to the trajectorycandidate, measure a distance between the trajectory candidate and theobstacle, calculates a safety cost associated with the trajectorycandidate based on the distance between the trajectory candidate and theobstacle, and calculates a total trajectory cost for the trajectorycandidate based on the objective cost and the safety cost. The targettrajectory can then be selected from the trajectory candidates as havinga lowest total trajectory cost.

In one embodiment, for each of the trajectory candidates, processinglogic further determines a changing rate of acceleration along thetrajectory candidate, calculates a comfort cost for the trajectorycandidate based on the changing rate of acceleration along thetrajectory candidate, and calculates a total trajectory cost for thetrajectory candidate based on the objective cost and the comfort cost.The target trajectory can then be selected from the trajectorycandidates as having a lowest total trajectory cost. In anotherembodiment, calculating a comfort cost for the trajectory candidateincludes segmenting the trajectory candidate into a number of candidatesegments, for each of the candidate segments, calculating a segmentcomfort cost for the candidate segment based on a changing rate of anacceleration associated with the candidate segment, and calculating thecomfort cost based on the segment comfort costs of the candidatesegments. In another embodiment, the comfort cost is calculated based ona sum of segment comfort costs of the candidate segments.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 12 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110, or anyof servers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include 10 devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, trajectory generation module 308 and/ortrajectory evaluation module 309 of FIG. 3A. Processingmodule/unit/logic 1528 may also reside, completely or at leastpartially, within memory 1503 and/or within processor 1501 duringexecution thereof by data processing system 1500, memory 1503 andprocessor 1501 also constituting machine-accessible storage media.Processing module/unit/logic 1528 may further be transmitted or receivedover a network via network interface device 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method of determining atrajectory to operate an autonomous driving vehicle, the methodcomprising: generating a plurality of trajectory candidates for anautonomous driving vehicle (ADV) from a starting point to an end pointof a particular driving scenario; generating a reference trajectorycorresponding to the driving scenario based on a current state of theADV associated with the starting point and an end state of the ADVassociated with the end point, wherein the reference trajectory isassociated with an objective for an amount of time for the ADV to reachthe end point; for each of the trajectory candidates, comparingvelocities of the trajectory candidate with corresponding velocities ofthe reference trajectory to generate an objective cost representing asimilarity between the trajectory candidate and the referencetrajectory; and selecting one of the trajectory candidates as a targettrajectory for driving the ADV based on objective costs of thetrajectory candidates.
 2. The method of claim 1, wherein comparingvelocities of the trajectory candidate with corresponding velocities ofthe reference trajectory comprises: segmenting the trajectory candidateinto a plurality of candidate segments; segmenting the referencetrajectory into a plurality of reference segments; for each of thecandidate segments, calculating a velocity difference between a velocityof the candidate segment and a velocity of a corresponding referencesegment; and calculating the objective cost based on the velocitydifferences between the candidate segments and reference segments. 3.The method of claim 2, wherein the objective cost is calculated bysumming squared velocity differences between the candidate segments andthe reference segments.
 4. The method of claim 1, further comprising:for each of the trajectory candidates, identifying an obstacle that isclosest to the trajectory candidate, measuring a distance between thetrajectory candidate and the obstacle, calculating a safety costassociated with the trajectory candidate based on the distance betweenthe trajectory candidate and the obstacle, and calculating a totaltrajectory cost for the trajectory candidate based on the objective costand the safety cost; and selecting the target trajectory from thetrajectory candidates, the target trajectory having a lowest totaltrajectory cost of the trajectory candidates.
 5. The method of claim 1,further comprising: for each of the trajectory candidates, determining achanging rate of acceleration along the trajectory candidate,calculating a comfort cost for the trajectory candidate based on thechanging rate of acceleration along the trajectory candidate, andcalculating a total trajectory cost for the trajectory candidate basedon the objective cost and the comfort cost; and selecting the targettrajectory from the trajectory candidates, the target trajectory havinga lowest total trajectory cost of the trajectory candidates.
 6. Themethod of claim 5, wherein calculating a comfort cost for the trajectorycandidate comprises: segmenting the trajectory candidate into aplurality of candidate segments; for each of the candidate segments,calculating a segment comfort cost for the candidate segment based on achanging rate of an acceleration associated with the candidate segment;and calculating the comfort cost based on the segment comfort costs ofthe candidate segments.
 7. The method of claim 6, wherein the comfortcost is calculated based on a sum of segment comfort costs of thecandidate segments.
 8. A non-transitory machine-readable medium havinginstructions stored therein, which when executed by one or moreprocessors, cause the one or more processors to perform operations, theoperations comprising: generating a plurality of trajectory candidatesfor an autonomous driving vehicle (ADV) from a starting point to an endpoint of a particular driving scenario; generating a referencetrajectory corresponding to the driving scenario based on a currentstate of the ADV associated with the starting point and an end state ofthe ADV associated with the end point, wherein the reference trajectoryis associated with an objective for an amount of time for the ADV toreach the end point; for each of the trajectory candidates, comparingvelocities of the trajectory candidate with corresponding velocities ofthe reference trajectory to generate an objective cost representing asimilarity between the trajectory candidate and the referencetrajectory; and selecting one of the trajectory candidates as a targettrajectory for driving the ADV based on objective costs of thetrajectory candidates.
 9. The non-transitory machine-readable medium ofclaim 8, wherein comparing velocities of the trajectory candidate withcorresponding velocities of the reference trajectory comprises:segmenting the trajectory candidate into a plurality of candidatesegments; segmenting the reference trajectory into a plurality ofreference segments; for each of the candidate segments, calculating avelocity difference between a velocity of the candidate segment and avelocity of a corresponding reference segment; and calculating theobjective cost based on the velocity differences between the candidatesegments and reference segments.
 10. The non-transitory machine-readablemedium of claim 9, wherein the objective cost is calculated by summingsquared velocity differences between the candidate segments and thereference segments.
 11. The non-transitory machine-readable medium ofclaim 8, further comprising: for each of the trajectory candidates,identifying an obstacle that is closest to the trajectory candidate,measuring a distance between the trajectory candidate and the obstacle,calculating a safety cost associated with the trajectory candidate basedon the distance between the trajectory candidate and the obstacle, andcalculating a total trajectory cost for the trajectory candidate basedon the objective cost and the safety cost; and selecting the targettrajectory from the trajectory candidates, the target trajectory havinga lowest total trajectory cost of the trajectory candidates.
 12. Thenon-transitory machine-readable medium of claim 8, further comprising:for each of the trajectory candidates, determining a changing rate ofacceleration along the trajectory candidate, calculating a comfort costfor the trajectory candidate based on the changing rate of accelerationalong the trajectory candidate, and calculating a total trajectory costfor the trajectory candidate based on the objective cost and the comfortcost; and selecting the target trajectory from the trajectorycandidates, the target trajectory having a lowest total trajectory costof the trajectory candidates.
 13. The non-transitory machine-readablemedium of claim 12, wherein calculating a comfort cost for thetrajectory candidate comprises: segmenting the trajectory candidate intoa plurality of candidate segments; for each of the candidate segments,calculating a segment comfort cost for the candidate segment based on achanging rate of an acceleration associated with the candidate segment;and calculating the comfort cost based on the segment comfort costs ofthe candidate segments.
 14. The non-transitory machine-readable mediumof claim 13, wherein the comfort cost is calculated based on a sum ofsegment comfort costs of the candidate segments.
 15. A data processingsystem, comprising: one or more processors; and a memory coupled to theone or more processors to store instructions, which when executed by theone or more processors, cause the one or more processors to performoperations, the operations including generating a plurality oftrajectory candidates for an autonomous driving vehicle (ADV) from astarting point to an end point of a particular driving scenario;generating a reference trajectory corresponding to the driving scenariobased on a current state of the ADV associated with the starting pointand an end state of the ADV associated with the end point, wherein thereference trajectory is associated with an objective for an amount oftime for the ADV to reach the end point; for each of the trajectorycandidates, comparing velocities of the trajectory candidate withcorresponding velocities of the reference trajectory to generate anobjective cost representing a similarity between the trajectorycandidate and the reference trajectory; and selecting one of thetrajectory candidates as a target trajectory for driving the ADV basedon objective costs of the trajectory candidates.
 16. The system of claim15, wherein comparing velocities of the trajectory candidate withcorresponding velocities of the reference trajectory comprises:segmenting the trajectory candidate into a plurality of candidatesegments; segmenting the reference trajectory into a plurality ofreference segments; for each of the candidate segments, calculating avelocity difference between a velocity of the candidate segment and avelocity of a corresponding reference segment; and calculating theobjective cost based on the velocity differences between the candidatesegments and reference segments.
 17. The system of claim 16, wherein theobjective cost is calculated by summing squared velocity differencesbetween the candidate segments and the reference segments.
 18. Thesystem of claim 15, further comprising: for each of the trajectorycandidates, identifying an obstacle that is closest to the trajectorycandidate, measuring a distance between the trajectory candidate and theobstacle, calculating a safety cost associated with the trajectorycandidate based on the distance between the trajectory candidate and theobstacle, and calculating a total trajectory cost for the trajectorycandidate based on the objective cost and the safety cost; and selectingthe target trajectory from the trajectory candidates, the targettrajectory having a lowest total trajectory cost of the trajectorycandidates.
 19. The system of claim 15, further comprising: for each ofthe trajectory candidates, determining a changing rate of accelerationalong the trajectory candidate, calculating a comfort cost for thetrajectory candidate based on the changing rate of acceleration alongthe trajectory candidate, and calculating a total trajectory cost forthe trajectory candidate based on the objective cost and the comfortcost; and selecting the target trajectory from the trajectorycandidates, the target trajectory having a lowest total trajectory costof the trajectory candidates.
 20. The system of claim 19, whereincalculating a comfort cost for the trajectory candidate comprises:segmenting the trajectory candidate into a plurality of candidatesegments; for each of the candidate segments, calculating a segmentcomfort cost for the candidate segment based on a changing rate of anacceleration associated with the candidate segment; and calculating thecomfort cost based on the segment comfort costs of the candidatesegments.
 21. The system of claim 20, wherein the comfort cost iscalculated based on a sum of segment comfort costs of the candidatesegments.